A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills

Yinghan Chen, Steven Andrew Culpepper, Shiyu Wang, Jeffrey Douglas

Research output: Contribution to journalArticlepeer-review

Abstract

The increasing presence of electronic and online learning resources presents challenges and opportunities for psychometric techniques that can assist in the measurement of abilities and even hasten their mastery. Cognitive diagnosis models (CDMs) are ideal for tracking many fine-grained skills that comprise a domain, and can assist in carefully navigating through the training and assessment of these skills in e-learning applications. A class of CDMs for modeling changes in attributes is proposed, which is referred to as learning trajectories. The authors focus on the development of Bayesian procedures for estimating parameters of a first-order hidden Markov model. An application of the developed model to a spatial rotation experimental intervention is presented.

Original languageEnglish (US)
Pages (from-to)5-23
Number of pages19
JournalApplied Psychological Measurement
Volume42
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • Latent class models
  • cognitive diagnosis
  • learning

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)

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